人工智能辅助《大学物理》课堂可视化教学研究

Research on Visualized Teaching of College Physics Classrooms Assisted by Artificial Intelligence

摘要: 《大学物理》课程覆盖力学、热学、电磁学、光学和近代物理等内容，具有公式抽象、时空图像复杂和实验资源受限等特点。针对传统课堂中学生难以建立动态物理图像、理论课与实验课衔接不足等问题，本文围绕教材中的机械振动与波、气体动理论、静电场、恒定磁场和光学等章节，探索了以轻量化虚拟仿真实验网页为载体的人工智能(AI)辅助大学物理课堂可视化教学路径。网页集成麦克斯韦速率分布、点电荷系统电场、波的干涉与相位控制、洛伦兹力与磁场圆周运动等模块，支持多维度参数调节和可视化图像呈现。初步教学实践表明，AI辅助可视化资源有助于学生理解抽象物理概念、建立物理图像，可为大学物理课堂教学可视化提供一种低成本、易推广的实践方案。

Abstract: College Physics covers mechanics, thermodynamics, electromagnetism, optics, and modern physics. It is characterized by abstract formulas, complex spatiotemporal images, and limited experimental resources. To address the difficulties students face in constructing dynamic physical images in traditional classrooms and the insufficient connection between theoretical instruction and experimental teaching, this study explores an artificial intelligence (AI)-assisted visualization teaching pathway for College Physics classrooms using a lightweight virtual simulation webpage as the instructional carrier. Based on chapters including mechanical vibrations and waves, kinetic theory of gases, electrostatic fields, steady magnetic fields, and optics, the webpage integrates modules such as Maxwell’s speed distribution, the electric field of point charge systems, wave interference and phase control, and Lorentz force and circular motion in a magnetic field. It supports multidimensional parameter adjustment and visualized image presentation. Preliminary teaching practice indicates that AI-assisted visualization resources help students understand abstract physical concepts and construct physical images. This approach provides a low-cost and easily scalable practical scheme for visualized teaching in College Physics classrooms.